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method, the LLNA value was accepted as representative for the structure; where the
LLNA test was not available, the GPMT value was taken (Patlewicz et al., 2007, Roberts
et al., 2007).
1.2.2.3. AMES Mutagenicity model
The mutagenicity model (Serafimova et al., 2006) was derived on the basis of 345
chemicals that were found experimentally to be positive without S-9 metabolic system
and a further 2357 chemicals – including 2012 negatives, and 397 positives with S-9
system. The simulator used for predicting metabolic activation of chemicals under S-9 is
in fact predicting a mammalian liver metabolism where the settings of the simulator were
adjusted in a way to avoid missing mutagenic metabolites. The simulator used in the
model mimics formation of enzyme complexes and channeling effects. According to the
adopted modeling scheme, the target chemicals are submitted to a metabolic simulator
and generated metabolites are subsequently screened by the 3D QSAR model to identify
mutagenic metabolites.
1.2.2.4. Model for Chromosomal aberrations
The model for chromosomal aberrations (CA) accounts for two principal types of
interaction mechanisms – interactions with DNA and interactions with proteins or nuclear
enzymes (Mekenyan et al., 2007). The model is used to predict metabolism in rat liver.
The alerting groups associated with these mechanisms are defined by specific structural
boundaries as well as by 2D and 3D parameter ranges describing effects of bioavailability
and reactivity alerts that are conditioned by the rest of the molecule. The model was
derived on the basis of 497 (166 positive and 331 negative) chemicals that have
experimental data without S-9 metabolic system and other 162 chemicals – including 81
positives, and 81 negatives with S-9 system. The performance of the model without
metabolic activation was characterized by sensitivity and specificity values of 77% and
82%, respectively. For the model coupled with the metabolic simulator (trained to
reproduce documented maps for mammalian (mainly rat) liver metabolism of 332
chemicals), the performance is 75% and 60% for sensitivity and specificity respectively.
1.2.3 OASIS LMC Models: Toxicological models - Receptor mediated effects
1.2.3.1. Androgen Binding Affinity QSAR model
Some of the environmental and industrial chemicals can interact with the androgen
receptor (AR) by mimicking the functions of natural hormones. The multiparameter
formulation of COmmon REactivity PAttern (COREPA) (Mekenyan et al., 2004)
approach was used to describe the structural requirements for eliciting androgen potency.
A structurally diverse training data set containing 202 chemicals was obtained from the
National Center for Toxicology Research (NCTR, US). The chemical affinities for the rat
AR were related to distances between nucleophilic sites and structural features describing
electronic interactions between the receptor and ligands.
1.2.3.2. Estrogen binding affinity QSAR model
The multiparameter formulation of COmmon REactivity PAttern (COREPA) (Mekenyan
et al., 2004) approach was used to describe the structural requirements for eliciting
estrogen binding potency. A training set of 645 chemicals which includes 497 steroid and
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